Ranking methods for entity‐oriented semantic web search

Koumenides, Christos L.; Shadbolt, Nigel

doi:10.1002/asi.23018

Cited by 7 publications

(4 citation statements)

References 56 publications

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“…In the GM approach, experts are represented as nodes, and their relationships are represented by their edges or implicitly derived from a graph. Different algorithms were used in the GM approach, such as Hyperlink-Induced Topic Search (HITS) [10,11,25] and PageRank [26]. For expert finding, PageRank was adapted in the context of online community discussions on a user-user graph built based on votes from users whose questions were answered by whom [27].…”

Section: Related Workmentioning

confidence: 99%

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

Kang¹,

Du²,

Forkan³

et al. 2021

Preprint

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Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose ExpFinder, a new ensemble model for expert finding, that integrates a novel N -gram vector space model, denoted as nVSM, and a graph-based model, denoted as µCO-HITS, that is a proposed variation of the CO-HITS algorithm. The key of nVSM is to exploit recent inverse document frequency weighting method for N -gram words, and ExpFinder incorporates nVSM into µCO-HITS to achieve expert finding. We comprehensively evaluate ExpFinder on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that ExpFinder is an highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.

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Section: Related Workmentioning

confidence: 99%

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

Kang¹,

Du²,

Forkan³

et al. 2021

Preprint

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“…Koumenides et al Hasibi et al [14] shows that entity linking can improve entity retrieval models. Asi et al [17] gives a comprehensive overview of ER approaches.…”

Section: Related Workmentioning

confidence: 99%

CACAO: Conditional Spread Activation for Keyword Factual Query Interpretation

Marx

Publio

Riechert

2019

Lecture Notes in Computer Science

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Information retrieval is regarded as pivotal to empower lay users to access the Web of Data. Over the past years, it achieved momentum with a large number of approaches being developed for different scenarios such as entity retrieval, question answering, and entity linking. This work copes with the problem of entity retrieval over RDF knowledge graphs using keyword factual queries. It discloses an approach that incorporates keyword graph structure dependencies through a conditional spread activation. Experimental evaluation on standard benchmarks demonstrates that the proposed method can improve the performance of current state-of-the-art entity retrieval approaches reasonably.

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“…Shekarpour et al [29] applied the Hidden Markov Model to map question terms into entities and relations in the knowledge graph and translated keyword queries into structured SPARQL queries. For comprehensive overview of approaches to entity retrieval, we refer an interested reader to the recent surveys [17] [27]. Multi-fielded retrieval models.…”

Section: Related Workmentioning

confidence: 99%

Fielded Sequential Dependence Model for Ad-Hoc Entity Retrieval in the Web of Data

Zhiltsov

Kotov

Nikolaev

2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Previously proposed approaches to ad-hoc entity retrieval in the Web of Data (ERWD) used multi-fielded representation of entities and relied on standard unigram bag-of-words retrieval models. Although retrieval models incorporating term dependencies have been shown to be significantly more effective than the unigram bag-of-words ones for ad hoc document retrieval, it is not known whether accounting for term dependencies can improve retrieval from the Web of Data. In this work, we propose a novel retrieval model that incorporates term dependencies into structured document retrieval and apply it to the task of ERWD. In the proposed model, the document field weights and the relative importance of unigrams and bigrams are optimized with respect to the target retrieval metric using a learning-to-rank method. Experiments on a publicly available benchmark indicate significant improvement of the accuracy of retrieval results by the proposed model over state-of-the-art retrieval models for ERWD.

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Ranking methods for entity‐oriented semantic web search

Cited by 7 publications

References 56 publications

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

CACAO: Conditional Spread Activation for Keyword Factual Query Interpretation

Fielded Sequential Dependence Model for Ad-Hoc Entity Retrieval in the Web of Data

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